This server provides FDA and NHTSA regulatory-risk intelligence for AI agents, offering 14 tools to assess medical device risk, compliance, evidence, postmarket safety, reimbursement, and vehicle safety history.
Device Risk Lookup: Assess FDA compliance risk for a device category by product code, including recalls, MAUDE adverse-event trends, warning letters, and a composite risk score.
Firm Compliance History: Build a timeline of FDA public records for a firm, including recalls, warning letters, and Form 483 citations.
Watchlist Diff: Monitor machine-generated changes in FDA public records for specified product codes since a date.
Device Evidence Lookup: Retrieve structured premarket evidence for a device by 510(k) or De Novo number, including validation study design, sample sizes, endpoints, and performance metrics with verbatim source quotes.
Evidence Search: Search AI/ML device clearances by product code, panel, applicant, clinical data presence, sensitivity metrics, or PCCP.
Predicate Chain: Trace predicate ancestry of a 510(k) device, showing each predicate's age at clearance.
Evidence Cohort Stats: Get reporting-rate statistics across the AI/ML device corpus, optionally filtered by advisory panel.
Device Postmarket Lookup: Get post-clearance intelligence for a device, including recalls, MAUDE trends, warning letters, 483 matches, and drift signals.
Postmarket Search: Find devices by postmarket criteria such as drift signals, recalls in 24 months, or rising MAUDE trend.
Cohort Postmarket Stats: Get postmarket presence rates across the AI/ML cohort (recalls, rising MAUDE trends, drift signals, warning-letter matches), optionally by panel.
Reimbursement Lookup: Trace reimbursement pathways for a device by K/DEN or CPT code, including NTAP, CPT categories, CMS rates, HCPCS, and LCDs.
Reimbursement Search: Find payment pathways by mechanism, CPT category, NTAP status, or applicant.
Reimbursement Stats: Get distribution of payment mechanisms and dollar ranges across the reimbursement corpus.
Vehicle Risk Lookup: Look up NHTSA safety history for a vehicle by make, model, and year, including recalls and complaint statistics.
The MCP server behind Constat — FDA & NHTSA regulatory-risk intelligence for AI agents, over the Model Context Protocol. Live public regulatory data — recalls, adverse events, warning letters, 510(k) premarket evidence, postmarket drift signals, reimbursement pathways, and vehicle safety. Decision support, not regulatory advice.
Endpoint: https://constat.dev/api/mcp (legacy https://radar.healthai.com/api/mcp still serves)
Transport: Streamable HTTP (JSON-RPC 2.0)
Auth: none for anonymous access (10 work units/min and 50/day per
anonymous source); an optional X-API-Key header enables the separately
assigned metered allowance. Tool calls have documented work-unit costs.
Registry: com.healthai/radar (official MCP registry)
The evidence corpus and tool logic run hosted at the endpoint above. This repo
also ships server.mjs, a zero-dependency stdio bridge to that endpoint, so
stdio-only MCP clients can use the server like any local one:
node server.mjs # stdio MCP server, bridges to constat.dev/api/mcp// e.g. in an MCP client config
{ "mcpServers": { "constat": { "command": "node", "args": ["/path/to/server.mjs"] } } }docker build -t constat-mcp . && docker run -i constat-mcp # same, containerizedTool | What it does |
| FDA compliance risk for a device category by 3-letter product code — recalls, MAUDE trend, warning-letter matches, composite score |
| Source-bounded FDA public-record timeline for a device firm — recalls, warning letters, 483s, clearances |
| Machine-detected FDA public-record changes for monitored product codes since a given date |
| Parsed 510(k) premarket evidence for an AI/ML device, each field with a verbatim source quote + page |
| Find AI/ML clearances by product code, panel, applicant, clinical data, sensitivity metric, or PCCP |
| Trace a device's predicate ancestry with each predicate's age at clearance |
| Reporting-rate stats across the parsed AI/ML corpus — presence figures with denominators |
| Post-clearance intelligence for one device — recalls, MAUDE trend, letter/483 matches, drift signals |
| Find devices by postmarket criteria — drift signals, recalls in 24mo, rising MAUDE trend |
| Postmarket presence rates across the AI/ML cohort, each with its denominator |
| Clearance-to-payment pathway by K/DEN or CPT code — NTAP, Cat I/III, CMS rates, HCPCS, LCDs |
| Find payment pathways by mechanism, CPT category, NTAP status, applicant |
| Mechanism distribution across the reimbursement corpus with dollar ranges |
| NHTSA safety history by make/model/year — recall campaigns and complaint stats |
Related MCP server: fedramp-docs-mcp
curl -s https://constat.dev/api/mcp \
-H 'content-type: application/json' \
-H 'accept: application/json, text/event-stream' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
"params":{"name":"device_risk_lookup","arguments":{"product_code":"FRN"}}}'List tools with {"method":"tools/list"}. Note the Accept header must include
text/event-stream (streamable-HTTP requirement).
Each of the 14 tools declares an output schema. Tool calls return structured
content with an explicit ok, not_found, invalid_request, or unavailable
status while preserving a text result for older clients.
The tools are observational and non-destructive, but advertise
readOnlyHint: false because calls write bounded quota and operational-
telemetry state. They do not publish or modify FDA, CMS, NHTSA, or other
third-party records.
See the integration guide, architecture and trust boundaries, privacy notice, and support page.
Clarity MCP — condition-aware ingredient, product & supplement safety (verdict + evidence tier + citation).
MCP Queen — the graded MCP registry that independently probes and grades this server.
Built by Health AI, the team behind Constat. Informational only; verify against primary FDA/NHTSA sources.
MIT — see LICENSE.
Postmarket presence rates across the snapshotted AI/ML device cohort (optionally by panel): share with any recall in 24 months, with a rising MAUDE trend, with any drift signal, with a warning-letter match — every rate with its denominator inline, never pooled across devices.
| Name | Required | Description | Default |
|---|---|---|---|
| panel | No | Advisory panel, e.g. Radiology; omit for all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that rates are per-device with denominator inline and based on a snapshotted cohort. However, it does not mention authentication, rate limits, data freshness, or side effects. The disclosure is helpful but incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the main purpose. It is efficient but somewhat dense due to the colon and dash. Every phrase adds value, though readability could be improved slightly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one optional parameter and no output schema, the description explains the output metrics and denominator policy. However, it does not cover data source, update frequency, or interpretation guidance. For a statistical tool, additional context would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single optional parameter 'panel', with a description already stating 'Advisory panel, e.g. Radiology; omit for all'. The description adds 'optionally by panel' which aligns with schema, but provides no extra semantic detail beyond what the schema offers.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it computes postmarket presence rates for an AI/ML device cohort, listing specific metrics (recall, MAUDE trend, drift signal, warning-letter match). It specifies the resource and action, though it doesn't explicitly differentiate from sibling tools like device_postmarket_lookup or evidence_cohort_stats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description implies cohort-level analysis, but does not mention exclusions or provide comparison with sibling tools such as device_postmarket_lookup for individual devices.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up the structured premarket evidence FDA accepted for a specific AI/ML-enabled device by 510(k) number (e.g. K252148). Returns parsed summary fields — validation study design, sample sizes, endpoints, reported performance, predicate chain, PCCP — each with a verbatim source quote and page. Null means the summary did not state it.
| Name | Required | Description | Default |
|---|---|---|---|
| k_number | Yes | 510(k) or De Novo number, e.g. K252148 or DEN180001 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully bears the burden. It discloses that the tool returns parsed summary fields with source quotes and page numbers, and clarifies that null means the information was not stated. This is sufficient for a safe, read-only operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences. The first sentence states the purpose and input, the second explains the output and null behavior. Every sentence adds unique information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple one-parameter input and no output schema, the description adequately covers the output structure (list of fields) and special cases (null). Minor gaps include lack of error handling or authentication details, but these are not critical for this lookup tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema defines the k_number parameter with pattern and description. The description adds value by providing an example (K252148) and specifying the domain (AI/ML-enabled devices), which clarifies the intended use beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool looks up structured premarket evidence for AI/ML-enabled devices using a 510(k) number. It lists the specific fields returned (validation study design, sample sizes, etc.) and distinguishes from sibling tools like device_risk_lookup and evidence_search by its focused scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a specific device number is known and premarket evidence is needed. It does not explicitly exclude other scenarios or mention alternatives, but the context from sibling tool names provides adequate guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Post-clearance intelligence for one AI/ML device by 510(k) number: its product code's recalls, MAUDE adverse-event level and trend, warning-letter and 483 matches for the applicant, plus per-device drift signals (adverse-event inflection, re-clearances of the same device line, software-recall patterns, predicate-cohort recall activity). Descriptive observables with sources — never a safety judgment.
| Name | Required | Description | Default |
|---|---|---|---|
| k_number | Yes | 510(k) or De Novo number, e.g. K252148 or DEN180001 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It states it returns 'descriptive observables with sources — never a safety judgment,' clarifying its non-judgmental nature. It lists all included data types, but lacks details on authentication, rate limits, or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence that efficiently lists all key points without any wasted words. Every clause adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description sufficiently explains return values (recalls, MAUDE, warning letters, drift signals) and notes sources. It is complete for a lookup tool, though an example would enhance completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with a clear pattern and description. The description adds 'by 510(k) number,' but does not provide additional meaning beyond the schema's specification.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it provides 'post-clearance intelligence' for a device by 510(k) number and lists specific data points (recalls, MAUDE, warning letters, drift signals). It distinguishes itself from sibling tools like 'device_risk_lookup' by focusing on post-clearance data and from 'postmarket_search' by being per-device.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need post-clearance data for a specific device by 510(k) number. It clearly defines the context but does not explicitly state when not to use it or provide alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Review a medical-device category's public FDA signals by three-letter product code (e.g. FRN = infusion pump). Returns recalls, MAUDE adverse-event trend, warning-letter matches, a normalized category signal, its driver contributions, and interpretation limits. It does not predict enforcement against a firm.
| Name | Required | Description | Default |
|---|---|---|---|
| product_code | Yes | FDA product code, e.g. FRN |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It lists outputs and a limitation, but does not explicitly state that the tool is read-only or describe any side effects, authentication, or rate limits. The negation about enforcement is useful but insufficient for full transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first states the core action and example, the second enumerates outputs and a key limitation. Every sentence is information-dense and front-loaded, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description adequately covers input semantics, output list, and a limitation. It could be slightly more precise about data scope (e.g., single category) but overall is comprehensive enough for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'product_code' has 100% schema coverage, but the description adds semantic value by providing an example ('FRN = infusion pump') and noting it is three-letter, reinforcing the schema constraints beyond the schema's description field.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: review FDA signals for a medical-device category via product code. It lists specific returns (recalls, MAUDE trend, etc.) and provides an example ('FRN = infusion pump'), making the purpose unambiguous and well-distinguished from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives an implicit usage hint by requiring a product code and noting what the tool does not do ('does not predict enforcement against a firm'). However, it lacks explicit guidance on when to use this tool versus alternatives like device_postmarket_lookup or firm_compliance_history, leaving the agent to infer from the output description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Reporting-rate stats across the parsed AI/ML corpus (optionally by panel). Each rate is a presence figure with its denominator — 'reported in X of Y audited devices' — never a pooled performance value. Excludes not-yet-parsed devices from every denominator and discloses the parse queue separately. Predicate age (median years between a clearance and its cited predicates) is included when decision-date coverage clears a 60% floor, and withheld otherwise.
| Name | Required | Description | Default |
|---|---|---|---|
| panel | No | Advisory panel, e.g. Radiology; omit for all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden for behavioral disclosure. It explains that rates are reported as 'X of Y audited devices', excludes not-yet-parsed devices, discloses the parse queue separately, and details the condition for including predicate age (60% decision-date coverage). This provides clear insight into how the tool behaves, though auth and error handling are omitted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, each serving a purpose: stating the main function and optionality, clarifying the rate format, and explaining exclusions and predicate age condition. It is front-loaded and efficient, though the third sentence could be slightly more streamlined.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one optional parameter, no output schema, and no annotations, the description covers the essential aspects: what stats are provided, their format, exclusions, and a conditional element. It does not detail return structure or error cases, but for a stats tool this level of completeness is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The sole parameter 'panel' is described in the input schema as 'Advisory panel, e.g. Radiology; omit for all', and the description reinforces that it's optional and filters results. Since schema description coverage is 100%, the description adds minimal extra meaning beyond clarifying the optionality, but does not introduce new semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides reporting-rate stats across the parsed AI/ML corpus, optionally filtered by panel. It emphasizes that rates are presence figures with denominators, not pooled performance, which distinguishes it from other stats tools. The mention of predicate age conditional on coverage adds further specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for getting reporting rates on parsed devices, but it does not explicitly say when to use this vs. alternatives like cohort_postmarket_stats. No when-not-to-use guidance or comparisons to siblings are provided, leaving the agent to infer usage from the context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find AI/ML device clearances by filter — product code, panel, applicant, and whether the submission reported clinical data, any sensitivity metric, or a PCCP. Answers 'what evidence did FDA accept for devices like mine'. Returns matching records with their parsed evidence. Presence flags are descriptive: 'reports a sensitivity metric' is not 'reports a comparable sensitivity' — analysis units differ across devices.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| panel | No | Advisory panel, e.g. Radiology | |
| has_pccp | No | Included a Predetermined Change Control Plan | |
| applicant | No | Substring match on applicant/company name | |
| product_code | No | FDA product code, e.g. QAS | |
| has_clinical_data | No | ||
| reports_any_sensitivity_metric | No | Reported any sensitivity metric (canonical — includes per-finding sensitivities, not just the top-level slot). Not a claim of cross-device comparability. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses an important behavioral nuance: 'Presence flags are descriptive: reports a sensitivity metric is not reports a comparable sensitivity — analysis units differ across devices.' This warns the agent about interpretation of results. It also states it returns 'matching records with their parsed evidence.' However, it does not mention pagination, rate limits, or any destructive side effects (though none expected).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph of three sentences, front-loading the purpose and filters. Each sentence serves a purpose: stating action, listing filters, answering user intent, clarifying result semantics. It is concise with minimal redundancy, though it could be slightly more structured (e.g., separate sentences for the caveat).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 7 parameters, no annotations, and no output schema, the description covers the core filtering purpose and flag semantics. However, it lacks details on output format, pagination, and how to handle empty results. It does not differentiate from sibling tool 'evidence_cohort_stats' or 'device_evidence_lookup'. Completeness is adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 71% (5 of 7 parameters have descriptions). The description adds little beyond the schema: it groups parameters but does not provide new semantic details. For example, 'has_clinical_data' lacks a schema description and the tool description doesn't elaborate. Baseline 3 is appropriate since the schema already documents most parameters adequately.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Find AI/ML device clearances by filter' and lists specific filter criteria (product code, panel, applicant, clinical data, sensitivity metric, PCCP). It answers a user question ('what evidence did FDA accept for devices like mine'), making the purpose unambiguous and distinct from sibling tools like 'device_evidence_lookup' which likely targets a single device.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool: to search for evidence by filter criteria. It provides context ('answers what evidence did FDA accept for devices like mine') but does not explicitly state when to avoid it or mention alternatives among the 13 sibling tools. The guidance is clear enough for an agent to infer usage, but lacks exclusionary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Build a recent, source-bounded FDA public-record timeline for a device firm: matched recalls, warning letters, and Form 483 citations where exact FEI numbers are available. Product codes are discovered from Constat's AI/ML-device corpus or may be supplied explicitly. Returns attribution and coverage limits with the records; it is not a finding of noncompliance or a prediction of FDA action.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum records per source and events in the timeline (default 50). | |
| since | No | Earliest event date, YYYY-MM-DD. Defaults to five years ago. | |
| firm_name | Yes | FDA applicant or company name, e.g. 'Medtronic' | |
| fei_numbers | No | Optional exact FDA FEI numbers; improves Form 483 attribution. | |
| product_codes | No | Optional FDA product-code scope. When omitted, Constat discovers codes from applicant matches in its device corpus. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It discloses source-bounded nature, use of FEI numbers, product code discovery, and includes a caveat that it is not a finding of noncompliance. However, it could clarify data freshness or potential delays.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three sentences covering purpose, sources, constraints, and disclaimer. No wasted words; key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters and no output schema, the description covers purpose, sources, and limitations. It mentions 'returns attribution and coverage limits' but could elaborate on response format or pagination for completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds context about product code discovery from a corpus but does not provide additional specifics for individual parameters beyond what the schema already defines.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it builds a timeline of FDA records (recalls, warning letters, Form 483) with specific constraints (exact FEI numbers, product code discovery). It clearly distinguishes from sibling tools like device_evidence_lookup which are more general searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for firm compliance history but does not explicitly state when to use this tool versus alternatives like evidence_search or device_postmarket_lookup. No when-not or alternative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find AI/ML devices by postmarket criteria — product code, panel, applicant, whether any drift signal exists, minimum recalls in 24 months, or a rising MAUDE trend. Returns per-device postmarket summaries with drift-signal counts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| panel | No | Advisory panel, e.g. Radiology | |
| applicant | No | Substring match on applicant/company name | |
| product_code | No | FDA product code, e.g. QIH | |
| maude_trend_up | No | Code-level MAUDE trend above its own 3-year baseline | |
| has_drift_signal | No | ||
| min_recalls_24mo | No | Product-code recalls in trailing 24 months | |
| signal_specificity | No | Restrict the drift-signal filter to one tier: 'device' = attributable to this device (own line re-cleared, own applicant's recall, predicate neighbors) — the high-signal tier; 'product_code' = observed in its category |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states the output includes per-device summaries with drift-signal counts, implying a read-only operation. However, it does not disclose behavioral traits such as authentication needs, rate limits, or handling of empty results. Since no annotations are present, the description carries the burden but provides minimal behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally concise at two sentences, front-loading the purpose and summarizing the output. Every phrase adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 8 parameters, no output schema, and no annotations, the description is somewhat incomplete. It does not specify how filters combine (AND/OR), pagination, sorting, or the structure of the return value beyond basic summaries. It covers the essentials but leaves practical usage details unexplained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 75%, so the schema already documents most parameters. The description lists some parameter types (e.g., 'minimum recalls in 24 months') but does not add significant meaning beyond the schema. It omits details on how parameters interact or default behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Find AI/ML devices by postmarket criteria' and lists specific searchable fields including product code, panel, applicant, drift signal, recalls, and MAUDE trend. It clearly distinguishes from sibling tools like device_postmarket_lookup and cohort_postmarket_stats by being a search tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. Sibling tools are listed in context, but the description itself lacks explicit usage instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Trace the predicate ancestry of a 510(k) device, with each cited predicate's age (how many years old the predicate was when the child cleared). Reveals how AI/ML devices chain to older predicates.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | Max ancestry depth (default 4) | |
| k_number | Yes | 510(k) or De Novo number, e.g. K252148 or DEN180001 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It reveals that the output includes the age of each predicate and that it shows how AI/ML devices chain to older predicates. While it lacks detail on edge cases (e.g., missing predicates, depth limits), it provides meaningful behavioral insight beyond the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, no filler, and front-loads the primary action and resource. Every word adds value, making it efficient for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the specialized function and good schema coverage, the description covers the core purpose and distinctive features (predicate age, AI/ML focus). It lacks mention of output format or error behavior, but these are not critical for basic selection; it is sufficiently complete for the complexity level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% so the schema already documents both parameters (k_number and depth) with descriptions. The tool description does not add new semantic meaning to the parameters beyond what the schema provides, earning the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Trace' and identifies the resource as 'predicate ancestry of a 510(k) device'. It adds unique value by mentioning the age of predicates and the focus on AI/ML devices, clearly distinguishing it from sibling tools like device_risk_lookup or evidence_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for analyzing predicate chains and specifically highlights AI/ML devices, providing clear context. However, it does not explicitly state when not to use it or name alternative tools, missing a chance to guide the agent away from inappropriate uses.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Trace the clearance-to-payment pathway for an AI/ML device by FDA clearance number (K/DEN, e.g. DEN170073) OR bare CPT code (e.g. 75580). Returns every payment mechanism (NTAP add-on, Category I/III CPT + CMS rate, HCPCS, MAC LCD) with amounts, effective dates, and source links, plus any commercial/MAC payer coverage policies that reference the clearance or its codes. Answers 'who got paid, how much, through which mechanism, on what basis.' CPT codes are bare factual identifiers only — no procedure descriptors; follow the CMS source link for the official descriptor.
| Name | Required | Description | Default |
|---|---|---|---|
| cpt_code | No | Bare CPT code, e.g. 75580 or 0932T | |
| k_number | No | FDA clearance number, e.g. DEN170073 or K252148 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explains the outputs (payment mechanisms, amounts, dates, links) and clarifies that CPT codes are bare identifiers. However, it omits details on error handling, required permissions, rate limits, or side effects, leaving some transparency gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the main purpose, includes examples, and summarizes the answer. Every sentence adds meaningful information without fluff, making it concise yet comprehensive.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description covers inputs, outputs (payment mechanisms, amounts, dates, links), and special notes (CPT codes as bare identifiers). It lacks error handling or edge cases, but for a lookup tool, it provides sufficient context for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by providing examples (DEN170073, 75580) and explaining the function of each parameter (FDA clearance number vs. bare CPT code). It also warns that CPT codes are bare identifiers requiring official source lookup, going beyond schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Trace the clearance-to-payment pathway') and a clear resource (AI/ML device by FDA clearance number or CPT code). It defines what it returns (payment mechanisms, amounts, dates) and answers a specific question, distinguishing it from vague siblings like reimbursement_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains what the tool does but does not explicitly state when to use this tool over siblings like reimbursement_search or reimbursement_stats. It lacks exclusions or alternative guidance, leaving the agent to infer usage context from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find AI/ML device payment pathways by mechanism — e.g. 'devices that got NTAP', 'devices paid under a Category I CPT code', 'pathways with a known CMS dollar rate'. Filters: mechanism, CPT category, NTAP status, applicant. Returns pathways with amounts, effective dates, and sources. Use reimbursement_stats for the mechanism distribution (never a single pooled reimbursement rate).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| applicant | No | Substring match on device maker / applicant name | |
| mechanism | No | Payment mechanism | |
| ntap_status | No | ||
| cpt_category | No | CPT category | |
| has_cms_rate | No | Only pathways with a known CMS dollar rate |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the tool returns pathways with amounts, effective dates, and sources, and lists filters. However, it does not mention behavioral traits like pagination, rate limits, or what happens when no results are found. The description is adequate but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core purpose, then filters, then return info and usage guidance. Every sentence adds value, no fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 6 parameters, no output schema, and moderate complexity. The description covers the main purpose and filters, and mentions return fields. However, it does not explain the limit parameter behavior, pagination, or result structure. It is complete enough for basic use but lacks detail for nuanced queries.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 67% (4 of 6 parameters have descriptions). The tool description lists filter categories (mechanism, CPT category, NTAP status, applicant) but does not add significant detail beyond what the schema already provides. For the two undocumented parameters (limit, ntap_status), the description offers no extra clarity. Baseline 3 is appropriate given moderate coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds AI/ML device payment pathways by mechanism, with specific examples (e.g., 'devices that got NTAP', 'pathways with a known CMS dollar rate'). It distinguishes itself from sibling tool reimbursement_stats by noting that tool provides distribution, not a single rate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance to use reimbursement_stats for mechanism distribution, telling the agent when not to use this tool. It does not, however, address other sibling tools like reimbursement_lookup or evidence_search, but the provided alternative is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Distribution of payment mechanisms across the AI/ML reimbursement corpus — pathway and distinct-device counts per mechanism (NTAP, Cat I, Cat III/APC, …) with the min/median/max dollar amounts for each. Deliberately never a single pooled 'reimbursement rate': NTAP add-on amounts and CMS rates are different measurements and are reported separately with their own spreads.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and discloses key behavioral traits: it never gives a single pooled rate, and reports NTAP and CMS rates separately. Good transparency for what it does and does not do.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no wasted words. Efficiently conveys scope and behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and no output schema, the description fully explains what the tool returns (counts and dollar amounts per mechanism) and how it handles rate types, making it complete for a stat tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, schema coverage 100% (empty). Baseline 4 applies as description adds context about return values without needing parameter explanation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns distribution of payment mechanisms with counts and dollar amounts, using specific verbs like 'Distribution... counts... amounts'. It distinguishes from siblings by explicitly stating it never provides a single pooled rate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for reimbursement statistics and warns against expecting a pooled rate, but does not explicitly name alternative tools or when-not-to-use scenarios, though sibling names (e.g., reimbursement_lookup) give context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Look up NHTSA safety history for a vehicle by make, model, and model year. Returns recall campaigns and complaint statistics (crashes, fires, injuries, top components).
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | e.g. honda | |
| year | Yes | model year, e.g. 2020 | |
| model | Yes | e.g. civic |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose whether the operation is read-only, requires authentication, or has any side effects, leaving behavioral aspects unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no extraneous information, front-loading the core purpose and listing return types efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 3-parameter tool with no output schema, the description covers the basic inputs and outputs but lacks behavioral context, making it adequate but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with descriptions for each parameter, so the description adds minimal extra value beyond reiterating the parameters' purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool looks up NHTSA safety history for a vehicle using make, model, and model year, distinguishing it from siblings like device_risk_lookup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly tells when to use (for vehicle safety history) and the required parameters (make, model, year), but lacks explicit exclusions or alternative tool references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return machine-generated FDA public-record changes detected for monitored product codes since a caller-supplied date, plus each code's latest category snapshot and postmarket coverage. Defaults to Constat Radar's five-code watchlist and the last seven days. Analyst verdict text and internal review status are excluded; use next_since as the next polling cursor.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum changes to return (default 100). | |
| since | No | Return changes on or after this ISO date or UTC timestamp. Defaults to the last seven days; pass the prior response's next_since for exact polling. | |
| product_codes | No | FDA product codes to poll. Omit for the default Radar watchlist. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses that the tool returns only machine-generated changes, excluding analyst verdict and internal review status. It also explains the default behavior for product_codes and since, and hints at pagination via next_since. No destructive or hidden behaviors are omitted.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is exceptionally concise: two sentences that front-load the core purpose and immediately follow with defaults, exclusions, and usage guidance. Every sentence earns its place without any unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking an output schema, the description adequately outlines what is returned (changes, category snapshot, postmarket coverage) and what is omitted (analyst verdict, review status). Combined with the polling hint about next_since, this provides sufficient context for an agent to invoke and process results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by stating defaults ('Constat Radar's five-code watchlist and the last seven days') and the purpose of the since parameter for polling (use next_since). This provides context beyond the schema's field descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it returns 'FDA public-record changes detected for monitored product codes since a caller-supplied date, plus each code's latest category snapshot and postmarket coverage.' This clearly identifies the verb (return), resource (changes and snapshots), and scope (monitored product codes), distinguishing it from sibling tools that perform static lookups or searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions that analyst verdict and review status are excluded and that next_since should be used as a polling cursor, providing some usage context. However, it does not explicitly guide when to use this tool over specific siblings like device_risk_lookup or firm_compliance_history, leaving the selection to the agent's inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.5.1cohort_postmarket_statsdevice_evidence_lookupdevice_postmarket_lookupdevice_risk_lookupevidence_cohort_statsevidence_searchfirm_compliance_historypostmarket_searchpredicate_chainreimbursement_lookupreimbursement_searchreimbursement_statsvehicle_risk_lookupwatchlist_diffEach tool has a clearly distinct purpose: device-level premarket/postmarket lookups, firm compliance history, search across evidence/postmarket/reimbursement, statistical aggregates, predicate tracing, watchlist diff, and even a vehicle safety lookup. The detailed descriptions further minimize ambiguity.
Tool names follow a consistent pattern: domain prefix (device_, firm_, evidence_, etc.) plus operation suffix (lookup, search, stats, diff, chain). All snake_case. The sole outlier vehicle_risk_lookup still follows the same pattern, maintaining overall consistency.
14 tools is on the higher end of the ideal range (3-15) but each tool serves a specific, non-redundant purpose within the regulatory intelligence domain. The count is reasonable given the breadth of covered areas (premarket, postmarket, reimbursement, and vehicle safety).
The tool set covers the key aspects of medical device regulatory intelligence: premarket evidence, postmarket surveillance, reimbursement, firm compliance, and trend monitoring. However, the inclusion of vehicle_risk_lookup (NHTSA) is a slight domain mismatch, and there is no tool for listing or searching product codes/panels directly, though these are used as filters elsewhere.
Agent-native MCP server over 49M+ US public and government records, privacy-first, always current.
Hosted MCP server exposing US hospital procedure cost data to AI assistants
Hosted MCP server for live public-data APIs and Skills for AI agents.
Judged, citation-checked policy corpus over MCP. Keyless public reads; API key for AI tools.
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